# search.py
# ---------
# Licensing Information:  You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
# 
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).


"""
In search.py, you will implement generic search algorithms which are called by
Pacman agents (in searchAgents.py).
"""

import util

class SearchProblem:
    """
    This class outlines the structure of a search problem, but doesn't implement
    any of the methods (in object-oriented terminology: an abstract class).

    You do not need to change anything in this class, ever.
    """

    def getStartState(self):
        """
        Returns the start state for the search problem.
        """
        util.raiseNotDefined()

    def isGoalState(self, state):
        """
          state: Search state

        Returns True if and only if the state is a valid goal state.
        """
        util.raiseNotDefined()

    def getSuccessors(self, state):
        """
          state: Search state

        For a given state, this should return a list of triples, (successor,
        action, stepCost), where 'successor' is a successor to the current
        state, 'action' is the action required to get there, and 'stepCost' is
        the incremental cost of expanding to that successor.
        """
        util.raiseNotDefined()

    def getCostOfActions(self, actions):
        """
         actions: A list of actions to take

        This method returns the total cost of a particular sequence of actions.
        The sequence must be composed of legal moves.
        """
        util.raiseNotDefined()


def tinyMazeSearch(problem):
    """
    Returns a sequence of moves that solves tinyMaze.  For any other maze, the
    sequence of moves will be incorrect, so only use this for tinyMaze.
    """
    from game import Directions
    s = Directions.SOUTH
    w = Directions.WEST
    return  [s, s, w, s, w, w, s, w]
# dfs based backtrack
# def depthFirstSearch(problem: SearchProblem):
#     """
#     Search the deepest nodes in the search tree first.

#     Your search algorithm needs to return a list of actions that reaches the
#     goal. Make sure to implement a graph search algorithm.

#     To get started, you might want to try some of these simple commands to
#     understand the search problem that is being passed in:

#     print("Start:", problem.getStartState())
#     print("Is the start a goal?", problem.isGoalState(problem.getStartState()))
#     print("Start's successors:", problem.getSuccessors(problem.getStartState()))
#     """
#     "*** YOUR CODE HERE ***"
#     cur = problem.getStartState()
#     res = []
#     vis = set()
#     temp = []
    
#     def dfs(cur, problem: SearchProblem):
#         if vis.__contains__(cur):
#             return
#         vis.add(cur)
#         if problem.isGoalState(state=cur):
#             res.append(temp.copy())
#             return 
#         for successor in problem.getSuccessors(cur):
#             temp.append(successor[1])
#             dfs(successor[0], problem)
#             if len(res) > 0:
#                 return
#             temp.pop()
    
#     dfs(cur, problem)
#     if len(res) > 0:
#         return res[0]
#     else:
#         return None

# dfs based stack
def depthFirstSearch(problem: SearchProblem):
    stack = util.Stack()
    start = problem.getStartState()
    stack.push(start)
    vis = set()
    dst = None
    parent = {}
    flag = False

    while not stack.isEmpty():
        cur = stack.pop()
        vis.add(cur)
        if problem.isGoalState(state=cur):
            flag = True
            dst = cur
            break
        # print(cur)
        for successor in problem.getSuccessors(cur):
            # print(successor)
            if vis.__contains__(successor[0]):
                continue
            parent[successor[0]] = [cur, successor[1]]
            # in fact, stack.push() need to be stack.updateOrPush()
            stack.push(successor[0])
    
    res = []
    if flag:
        l = parent[dst]
        while l[0] != start:
            res.append(l[1])
            l = parent[l[0]]
        res.append(l[1])
        res.reverse()
        return res
    else:
        return None

def breadthFirstSearch(problem: SearchProblem):
    """Search the shallowest nodes in the search tree first."""
    "*** YOUR CODE HERE ***"
    queue = util.Queue()
    start = problem.getStartState()
    queue.push(start)
    vis = set()
    vis.add(start)
    dst = None
    parent = {}
    flag = False

    while not queue.isEmpty():
        cur = queue.pop()
        # print(cur)
        if problem.isGoalState(state=cur):
            flag = True
            dst = cur
            break
        for successor in problem.getSuccessors(cur):
            # print(successor)
            if vis.__contains__(successor[0]):
                continue
            vis.add(successor[0])
            parent[successor[0]] = [cur, successor[1]]
            queue.push(successor[0])
    
    res = []
    if flag:
        l = parent[dst]
        while l[0] != start:
            res.append(l[1])
            l = parent[l[0]]
        res.append(l[1])
        res.reverse()
        return res
    else:
        return None


def uniformCostSearch(problem: SearchProblem):
    """Search the node of least total cost first."""
    "*** YOUR CODE HERE ***"
    vis = {}
    start = problem.getStartState()
    vis[start] = 0
    f = lambda l : vis[l]
    open = util.PriorityQueue()
    open.push(start, f(start))
    parent = {}
    dst = None
    flag = False
    while not open.isEmpty():
        cur = open.pop()
        cost = vis[cur]
        if problem.isGoalState(cur):
            dst = cur
            flag = True
            break
        for successor in problem.getSuccessors(cur):
            if vis.__contains__(successor[0]):
                sucost = vis[successor[0]]
                if sucost > cost + successor[2]:
                    vis[successor[0]] = cost + successor[2]
                    parent[successor[0]] = [cur, successor[1]]
                    open.update(successor[0], vis[successor[0]])
            else:
                vis[successor[0]] = cost + successor[2]
                parent[successor[0]] = [cur, successor[1]]
                open.push(successor[0], f(successor[0]))
    
    res = []
    if flag:
        l = parent[dst]
        while l[0] != start:
            res.append(l[1])
            l = parent[l[0]]
        res.append(l[1])
        res.reverse()
        return res
    else:
        return None


def nullHeuristic(state, problem=None):
    """
    A heuristic function estimates the cost from the current state to the nearest
    goal in the provided SearchProblem.  This heuristic is trivial.
    """
    return 0
    
def aStarSearch(problem: SearchProblem, heuristic=nullHeuristic):
    """Search the node that has the lowest combined cost and heuristic first."""
    "*** YOUR CODE HERE ***"
    vis = {}
    start = problem.getStartState()
    vis[start] = 0
    def f(l): return vis[l] + heuristic(l, problem)
    open = util.PriorityQueue()
    open.push(start, f(start))
    parent = {}
    dst = None
    flag = False
    while not open.isEmpty():
        cur = open.pop()
        cost = vis[cur]
        if problem.isGoalState(cur):
            dst = cur
            flag = True
            break
        for successor in problem.getSuccessors(cur):
            gCost = cost + successor[2]
            if vis.__contains__(successor[0]):
                oldCost = f(successor[0])
                newCost = heuristic(successor[0], problem) + gCost
                if oldCost > newCost:
                    vis[successor[0]] = gCost
                    parent[successor[0]] = [cur, successor[1]]
                    open.update(successor[0], newCost)
            else:
                vis[successor[0]] = gCost
                parent[successor[0]] = [cur, successor[1]]
                open.push(successor[0], f(successor[0]))

    res = []
    if flag:
        l = parent[dst]
        while l[0] != start:
            res.append(l[1])
            l = parent[l[0]]
        res.append(l[1])
        res.reverse()
        return res
    else:
        return None


# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch
